Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article


Doctor of Philosophy




O'Donoghue, Patrick


Transfer RNAs (tRNAs) physically link the genetic code to an amino acid sequence, by recruiting amino acids to three-nucleotide codons in messenger RNAs. To ensure that the genetic code is translated as intended, tRNAs must be accurately aminoacylated and faithfully recognize codons in the ribosome during protein synthesis. Given the critical function of tRNAs, it has often been assumed that mutations in human tRNA genes would be either lethal to cells or not significantly impair tRNA function. My goal was to rigorously test this assumption in mammalian cell models, prompted by the recent discovery of unprecedented variation in human tRNA gene sequences.

First, I review existing knowledge on links between human cytosolic tRNA biology and disease. Next, I demonstrate that synthetic tRNA mutants can elicit significant levels of amino acid misincorporation in human cells, which is surprisingly well tolerated. I then test the effects of mistranslation by synthetic and natural tRNA variants on cellular models of neurodegenerative disease, based on our hypothesis that mistranslation would exacerbate protein-folding stress-associated diseases. I find that a natural tRNA variant occurring in ~2% of the sequenced human population has significant potential to modify the progression and severity of Huntington’s disease, amyotrophic lateral sclerosis, and potentially other diseases. Lastly, I investigate methodological approaches which could aid in the characterization of other natural human tRNA variants, while demonstrating that even identical tRNA variants may differ in phenotypic severity and effected tissues depending on local sequence context of the tRNA gene.

Summary for Lay Audience

Transfer RNAs, commonly known as tRNAs, are like assembly-line workers for the cell. Their job is to carry amino acids—the building blocks of proteins—to the ribosome, which assembles amino acids into a protein sequence. Proteins carry out most cellular functions. From providing structural support to the cell, to pumping ions across the cell membrane, their functions are astoundingly diverse. Hence, the job of tRNAs is critical to ensure that every protein is produced precisely as encoded by the human genome.

Given the critical function of tRNAs, it has often been assumed that mutations in tRNA genes would be either lethal to cells or not significantly impair tRNA function. However, since tRNAs are genetically encoded molecules themselves, they too are subject to mutation, and some mutations cause tRNAs to carry the wrong amino acids to the ribosome, or misread the genetic code, resulting in the erroneous incorporation of amino acids in proteins. Further, it was recently discovered that tRNA gene sequence variants are common in the human population, with the average individual harboring ~60-70 variants in tRNA genes compared to the human reference genome.

My thesis explores tRNA genes as an underappreciated source of variability in human diseases. First, I found that synthetic tRNA mutants can cause significant levels of amino acid misincorporation (~2-3%) without severely impacting cell viability. Next, I found that a naturally occurring tRNA variant found in ~2% of the sequenced human population had potent modifying effects on cellular models of Huntington’s disease and amyotrophic lateral sclerosis, demonstrating that tRNA variants have potential to modify the outcomes of neurodegenerative diseases. Lastly, I investigated several methodological approaches to characterize the many human tRNA sequence variants in the human population, while finding that even identical tRNA gene mutations can have differing severity of effects and in different cell types depending on which identical copy of a tRNA gene is mutated in the genome. Altogether, my work shines a new light on tRNA variants as potentially underappreciated modifiers of human disease and outlines new methods to study them.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

DataFileS3.1.mp4 (4983 kB)
Supp video file 1- Chapter 3